OpenRaiser/Intern-Atlas huggingface dataset:
DeepTracker: Visualizing the Training Process of Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Deep convolutional neural networks (CNNs) have achieved remarkable success in various fields. However, training an excellent CNN is practically a trial-and-error process that consumes a tremendous amount of time and computer resources. To accelerate the training process and reduce the number of trials, experts need to understand what has occurred in the training process and why the resulting CNN behaves as such. However, current popular training platforms, such as TensorFlow, only provide very little and general information, such as training/validation errors, which is far from enough to serve this purpose. To bridge this gap and help domain experts with their training tasks in a practical environment, we propose a visual analytics system, DeepTracker, to facilitate the exploration of the rich dynamics of CNN training processes and to identify the unusual patterns that are hidden behind the huge amount of training log. Specifically,we combine a hierarchical index mechanism and a set of hierarchical small multiples to help experts explore the entire training log from different levels of detail. We also introduce a novel cube-style visualization to reveal the complex correlations among multiple types of heterogeneous training data including neuron weights, validation images, and training iterations. Three case studies are conducted to demonstrate how DeepTracker provides its users with valuable knowledge in an industry-level CNN training process, namely in our case, training ResNet-50 on the ImageNet dataset. We show that our method can be easily applied to other state-of-the-art "very deep" CNN models.
Sensor-based, time-critical mobility of autonomous robots in cluttered spaces: a harmonic potential approach
Venue: arXiv cs.RO 2018
Year: 2018

This paper suggests an integrated navigation system for an unmanned ground vehicle operating in an unknown cluttered environment. The navigator supports time-critical mobility making it possible for a mobile robot to reach a target from the first attempt without the need for a dedicated exploration and mapping stage. The robot only uses necessary and sufficient egocentric local sensory data collected on its way to the target. The construction of the navigation structure revolves around key properties of the harmonic potential field approach to motion planning. The robots trajectory is well-behaved and direct-to-the-goal. It contains only the minimum number of detours necessary to accommodate the sensory data and maintain the robot in a safe, goal-oriented state. The navigation structure is developed and its theoretical basis is explained. Extensive experimental validation of its properties and performance is carried-out using the X80 robotic platform
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Venue: arXiv cs.LG 2018
Year: 2018

Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguistically-plausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets -- up to a 79.6% relative improvement -- while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.
Single Image Dehazing Based on Generic Regularity
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a novel technique for single image dehazing. Most of the state-of-the-art methods for single image dehazing relies either on Dark Channel Prior (DCP) or on Color line. The proposed method combines the two different approaches. We initially compute the dark channel prior and then apply a Nearest-Neighbor (NN) based regularization technique to obtain a smooth transmission map of the hazy image. We consider the effect of airlight on the image by using the color line model to assess the commitment of airlight in each patch of the image and interpolate at the local neighborhood where the estimate is unreliable. The NN based regularization of the DCP can remove the haze, whereas, the color line based interpolation of airlight effect makes the proposed system robust against the variation of haze within an image due to multiple light sources. The proposed method is tested on benchmark datasets and shows promising results compared to the state-of-the-art, including the deep learning based methods, which require a huge computational setup. Moreover, the proposed method outperforms the recent deep learning based methods when applied on images with sky regions.
Topology optimization of micro-structured materials featured with the specific mechanical properties
Venue: arXiv cs.CE 2018
Year: 2018

Micro-structured materials consisting of an array of microstructures are engineered to provide the specific material properties. This present work investigates the design of cellular materials under the framework of level set, so as to optimize the topologies and shapes of these porous material microstructures. Firstly, the energy-based homogenization method (EBHM) is applied to evaluate the material effective properties based on the topology of the material cell, where the effective elasticity property is evaluated by the average stress and strain theorems. Secondly, a parametric level set method (PLSM) is employed to optimize the microstructural topology until the specific mechanical properties can be achieved, including the maximum bulk modulus, the maximum shear modulus and their combinations, as well as the negative Poisson's ratio (NPR). The complicated topological shape optimization of the material microstructure has been equivalent to evolve the sizes of the expansion coefficients in the interpolation of the level set function. Finally, several numerical examples are fully discussed to demonstrate the effectiveness of the developed method. A series of new and interesting material cells with the specific mechanical properties can be found.
Reversing Parallel Programs with Blocks and Procedures
Venue: arXiv cs.PL 2018
Year: 2018

We show how to reverse a while language extended with blocks, local variables, procedures and the interleaving parallel composition. Annotation is defined along with a set of operational semantics capable of storing necessary reversal information, and identifiers are introduced to capture the interleaving order of an execution. Inversion is defined with a set of operational semantics that use saved information to undo an execution. We prove that annotation does not alter the behaviour of the original program, and that inversion correctly restores the initial program state.
Unique Solutions of Contractions, CCS, and their HOL Formalisation
Venue: arXiv cs.LO 2018
Year: 2018

The unique solution of contractions is a proof technique for bisimilarity that overcomes certain syntactic constraints of Milner's "unique solution of equations" technique. The paper presents an overview of a rather comprehensive formalisation of the core of the theory of CCS in the HOL theorem prover (HOL4), with a focus towards the theory of unique solutions of contractions. (The formalisation consists of about 20,000 lines of proof scripts in Standard ML.) Some refinements of the theory itself are obtained. In particular we remove the constraints on summation, which must be weakly-guarded, by moving to rooted contraction, that is, the coarsest precongruence contained in the contraction preorder.
A Parametric Framework for Reversible Pi-Calculi
Venue: arXiv cs.FL 2018
Year: 2018

This paper presents a study of causality in a reversible, concurrent setting. There exist various notions of causality in pi-calculus, which differ in the treatment of parallel extrusions of the same name. In this paper we present a uniform framework for reversible pi-calculi that is parametric with respect to a data structure that stores information about an extrusion of a name. Different data structures yield different approaches to the parallel extrusion problem. We map three well-known causal semantics into our framework. We show that the (parametric) reversibility induced by our framework is causally-consistent and prove a causal correspondence between an appropriate instance of the framework and Boreale and Sangiorgi's causal semantics.
Empirical Analysis of Common Subgraph Isomorphism Approaches to the Lost-in-Space Star Identification Problem
Venue: arXiv cs.DS 2018
Year: 2018

The process of identifying stars is integral toward stellar based orientation determination in spacecraft. Star identification involves matching points in an image of the sky with stars in an astronomical catalog. A unified framework for identification was created and used to analyze six variations of methods based on their approach to star set identification, obtaining a single image to catalog star set match, and uniquely mapping each star in a image star set to a catalog star set. Each method was presented an artificial image, and aspects that were interchangeable among each process were normalized. Given an image with false stars, the Pyramid method has the highest average accuracy and is the fastest of the six. Given an image where each star's true position is distributed randomly (Gaussian noise), the Spherical Triangle method's accuracy is the least sensitive.
Deeply Supervised Depth Map Super-Resolution as Novel View Synthesis
Venue: arXiv cs.CV 2018
Year: 2018

Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used; and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as $\times 8, \times 16$. In this paper, we propose a new framework to tackle the above problems. First, we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks. The novel view synthesis sub-task aims at generating (synthesizing) a depth map from different camera pose, which could be learned in parallel. Second, to handle large up-sampling factors, we present a deeply supervised network structure to enforce strong supervision in each stage of the network. Third, a multi-scale fusion strategy is proposed to effectively exploit the feature maps at different scales and handle the blocking effect. In this way, our proposed framework could deal with challenging depth map super-resolution efficiently under large up-sampling factors (e.g. $\times 8, \times 16$). Our method only uses the low-resolution depth map as input, and the support of color image is not needed, which greatly reduces the restriction of our method. Extensive experiments on various benchmarking datasets demonstrate the superiority of our method over current state-of-the-art depth map super-resolution methods.
Attentive Sequence to Sequence Translation for Localizing Clips of Interest by Natural Language Descriptions
Venue: arXiv cs.CV 2018
Year: 2018

We propose a novel attentive sequence to sequence translator (ASST) for clip localization in videos by natural language descriptions. We make two contributions. First, we propose a bi-directional Recurrent Neural Network (RNN) with a finely calibrated vision-language attentive mechanism to comprehensively understand the free-formed natural language descriptions. The RNN parses natural language descriptions in two directions, and the attentive model attends every meaningful word or phrase to each frame, thereby resulting in a more detailed understanding of video content and description semantics. Second, we design a hierarchical architecture for the network to jointly model language descriptions and video content. Given a video-description pair, the network generates a matrix representation, i.e., a sequence of vectors. Each vector in the matrix represents a video frame conditioned by the description. The 2D representation not only preserves the temporal dependencies of frames but also provides an effective way to perform frame-level video-language matching. The hierarchical architecture exploits video content with multiple granularities, ranging from subtle details to global context. Integration of the multiple granularities yields a robust representation for multi-level video-language abstraction. We validate the effectiveness of our ASST on two large-scale datasets. Our ASST outperforms the state-of-the-art by $4.28\%$ in Rank$@1$ on the DiDeMo dataset. On the Charades-STA dataset, we significantly improve the state-of-the-art by $13.41\%$ in Rank$@1,IoU=0.5$.
Cooperative Communication based Connectivity Recovery for UAV Networks
Venue: arXiv cs.NI 2018
Year: 2018

UAV networks often partition into separated clusters due to the high node and link dynamic. As a result, network connectivity recovery is an important issue in this area. Existing solutions always need excessive movement of nodes and thus lead to low recovery efficiency in terms of the time and energy consumption. In this paper, we for the first time study the issue of how to utilize cooperative communication technology to improve the connectivity recovery efficiency in UAV networks. We propose a Cooperative Communication based Connectivity Recovery algorithm for UAV Networks, named C3RUN. The key novelty is C3RUN not only uses cooperative communication to enlarge node's communication range and thus achieve quick repair of network connectivity, but also enables nodes to proactively move to better places for ensuring the establishment of cooperative communication links. We conduct extensive simulations to evaluate the performance of C3RUN. The simulation results reveal that C3RUN can not only achieve connectivity recovery with less nodes and shorter distance to move, but also always finish recovery with less time, when comparing with existing work. Furthermore, C3RUN can achieve 100% success ratio for connectivity recovery.
Facial Information Recovery from Heavily Damaged Images using Generative Adversarial Network- PART 1
Venue: arXiv cs.CV 2018
Year: 2018

Over the past decades, a large number of techniques have emerged in modern imaging systems to capture the exact information of the original scene regardless of shake, motion, lighting conditions and etc., These developments have progressively addressed the acquisition of images in high speed and high resolutions. However, the various ineradicable real-time factors cause the degradation of the information and the quality of the acquired images. The available techniques are not intelligent enough to generalize this complex phenomenon. Hence, it is necessary to develop an intellectual framework to recover the possible information presented in the original scene. In this article, we propose a kernel free framework based on conditional-GAN to recover the information from the heavily damaged images. The degradation of images is assumed to be occurred by the combination of a various blur. Learning parameter of the cGAN is optimized by multi-component loss function that includes improved wasserstein loss with regression loss function. The generator module of this network is developed by using U-Net architecture with local Residual connections and global skip connection. Local connections and a global skip connection are implemented for the utilization of all stages of features. Generated images show that the network has the potential to recover the probable information of blurred images from the learned features. This research work is carried out as a part of our IOP studio software 'Facial recognition module'.
Extracting Sentiment Attitudes From Analytical Texts
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present the RuSentRel corpus including analytical texts in the sphere of international relations. For each document we annotated sentiments from the author to mentioned named entities, and sentiments of relations between mentioned entities. In the current experiments, we considered the problem of extracting sentiment relations between entities for the whole documents as a three-class machine learning task. We experimented with conventional machine-learning methods (Naive Bayes, SVM, Random Forest).
Analysis and optimal individual pitch control decoupling by inclusion of an azimuth offset in the multi-blade coordinate transformation
Venue: arXiv cs.SY 2018
Year: 2018

With the trend of increasing wind turbine rotor diameters, the mitigation of blade fatigue loadings is of special interest to extend the turbine lifetime. Fatigue load reductions can be partly accomplished using Individual Pitch Control (IPC) facilitated by the so-called Multi-Blade Coordinate (MBC) transformation. This operation transforms and decouples the blade load signals in a yaw- and tilt-axis. However, in practical scenarios, the resulting transformed system still shows coupling between the axes, posing a need for more advanced Multiple-Input Multiple-Output (MIMO) control architectures. This paper presents a novel analysis and design framework for decoupling of the non-rotating axes by the inclusion of an azimuth offset in the reverse MBC transformation, enabling the application of simple Single-Input Single-Output (SISO) controllers. A thorough analysis is given by including the azimuth offset in a frequency-domain representation. The result is evaluated on simplified blade models, as well as linearizations obtained from the NREL~5\nobreakdash-MW reference wind turbine. A sensitivity and decoupling assessment justify the application of decentralized SISO control loops for IPC. Furthermore, closed-loop high-fidelity simulations show beneficial effects on pitch actuation and blade fatigue load reductions.
Downstream Effects of Affirmative Action
Venue: arXiv cs.GT 2018
Year: 2018

We study a two-stage model, in which students are 1) admitted to college on the basis of an entrance exam which is a noisy signal about their qualifications (type), and then 2) those students who were admitted to college can be hired by an employer as a function of their college grades, which are an independently drawn noisy signal of their type. Students are drawn from one of two populations, which might have different type distributions. We assume that the employer at the end of the pipeline is rational, in the sense that it computes a posterior distribution on student type conditional on all information that it has available (college admissions, grades, and group membership), and makes a decision based on posterior expectation. We then study what kinds of fairness goals can be achieved by the college by setting its admissions rule and grading policy. For example, the college might have the goal of guaranteeing equal opportunity across populations: that the probability of passing through the pipeline and being hired by the employer should be independent of group membership, conditioned on type. Alternately, the college might have the goal of incentivizing the employer to have a group blind hiring rule. We show that both goals can be achieved when the college does not report grades. On the other hand, we show that under reasonable conditions, these goals are impossible to achieve even in isolation when the college uses an (even minimally) informative grading policy.
It's Like Python But: Towards Supporting Transfer of Programming Language Knowledge
Venue: arXiv cs.SE 2018
Year: 2018

Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programming language, but desire to author code in a different language. For this scenario, we postulate that an effective form of feedback is presented as a transfer from concepts in the first language to the second. Current programming environments do not support this form of feedback. In this study, we apply the theory of learning transfer to teach a language that programmers are less familiar with--such as R--in terms of a programming language they already know--such as Python. We investigate learning transfer using a new tool called Transfer Tutor that presents explanations for R code in terms of the equivalent Python code. Our study found that participants leveraged learning transfer as a cognitive strategy, even when unprompted. Participants found Transfer Tutor to be useful across a number of affordances like stepping through and highlighting facts that may have been missed or misunderstood. However, participants were reluctant to accept facts without code execution or sometimes had difficulty reading explanations that are verbose or complex. These results provide guidance for future designs and research directions that can support learning transfer when learning new programming languages.
Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening
Venue: arXiv cs.CR 2018
Year: 2018

As the Industrial Internet of Things (IIot) becomes more prevalent in critical application domains, ensuring security and resilience in the face of cyber-attacks is becoming an issue of paramount importance. Cyber-attacks against critical infrastructures, for example, against smart water-distribution and transportation systems, pose serious threats to public health and safety. Owing to the severity of these threats, a variety of security techniques are available. However, no single technique can address the whole spectrum of cyber-attacks that may be launched by a determined and resourceful attacker. In light of this, we consider a multi-pronged approach for designing secure and resilient IIoT systems, which integrates redundancy, diversity, and hardening techniques. We introduce a framework for quantifying cyber-security risks and optimizing IIoT design by determining security investments in redundancy, diversity, and hardening. To demonstrate the applicability of our framework, we present two case studies in water distribution and transportation a case study in water-distribution systems. Our numerical evaluation shows that integrating redundancy, diversity, and hardening can lead to reduced security risk at the same cost.